Embedded Online Optimization for Model Predictive Control at Megahertz Rates

J. L. Jerez, P. J. Goulart, S. Richter, G. Constantinides, E. C. Kerrigan and M. Morari

IEEE Transactions on Automatic Control, vol. 59, no. 12, pp. 3238-3251, December 2014.
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  author = {J. L. Jerez and P. J. Goulart and S. Richter and G. Constantinides and E. C. Kerrigan and M. Morari},
  title = {Embedded Online Optimization for Model Predictive Control at Megahertz Rates},
  journal = {IEEE Transactions on Automatic Control},
  year = {2014},
  volume = {59},
  number = {12},
  pages = {3238-3251},
  url = {http://dx.doi.org/10.1109/TAC.2014.2351991},
  doi = {10.1109/TAC.2014.2351991}

Faster, cheaper, and more power efficient optimization solvers than those currently possible using general-purpose techniques are required for extending the use of model predictive control (MPC) to resource-constrained embedded platforms. We propose several custom computational architectures for different first-order optimization methods that can handle linear-quadratic MPC problems with input, input-rate, and soft state constraints. We provide analysis ensuring the reliable operation of the resulting controller under reduced precision fixed-point arithmetic. Implementation of the proposed architectures in FPGAs shows that satisfactory control performance at a sample rate beyond 1 MHz is achievable even on low-end devices, opening up new possibilities for the application of MPC on embedded systems.